Apparatus and Method for Detecting Debatable Document

1. A method for detecting a debatable document performed in a computing device comprising one or more processors and a memory storing one or more programs to be executed by the one or more processors, the method comprising: receiving a document comprising one or more sentences; generating an embedding vector for each of words included in the document; and extracting features of the document from an embedding vector matrix comprising the embedding vectors for the words, and detecting debatability of the document from the extracted features through a detection model comprising a first-step convolutional neural network and a second-step convolutional neural network, wherein each of the first-step convolutional neural network and the second-step convolutional neural network comprises a convolution layer.

2. The method of claim 1 , wherein the detection model comprises the first-step convolutional neural network comprising a first convolution layer for outputting a first feature vector by performing a convolution operation between the embedding vector matrix and a plurality of filters, and a first pooling layer for outputting a second feature vector by performing sub-sampling to the first feature vector; and the second-step convolutional neural network comprising a second convolution layer for outputting a third feature vector by performing a convolution operation between the second feature vector and a plurality of filters, and a second pooling layer for outputting a fourth feature vector by performing sub-sampling to the third feature vector.

3. The method of claim 2 , wherein the first convolution layer and the second convolution layer perform the convolution operation using a hyperbolic tangent function or a rectified linear unit (ReLU) function as an activation function.

4. The method of claim 2 , wherein the first pooling layer and the second pooling layer perform the sub-sampling using a max pooling function.

5. The method of claim 2 , wherein the detection model further comprises: one or more fully-connected layers connected to the second pooling layer; and an output layer for outputting a discrimination value of the debatability of the document from outputs of the one or more fully-connected layers.

6. The method of claim 5 , wherein the output layer outputs the discrimination value using a softmax function as an activation function.

7. The method of claim 1 , wherein the generating of the embedding vector comprises converting each of the words included in the document into a one-hot vector, and generating the embedding vector for each of the words by a product of the embedding matrix and the one-hot vector for each of the words.

8. An apparatus for detecting debatable document, the apparatus comprising: one or more hardware processors and one or more computer readable media storing instructions that, when executed by the one or more hardware processors, cause the apparatus to: receive a document comprising one or more sentences; generate an embedding vector for each of words included in the document; and extract features of the document from an embedding vector matrix comprising the embedding vectors for the words, and detect debatability of the document from the extracted features through a detection model comprising a first-step convolutional neural network and a second-step convolutional neural network, wherein each of the first-step convolutional neural network and the second-step convolutional neural network comprises a convolution layer.

9. The apparatus of claim 8 , wherein the detection model comprises: the first-step convolutional neural network comprising a first convolution layer for outputting a first feature vector by performing a convolution operation between the embedding vector matrix and a plurality of filters, and a first pooling layer for outputting a second feature vector by performing sub-sampling to the first feature vector; and the second-step convolutional neural network comprising a second convolution layer for outputting a third feature vector by performing a convolution operation between the second feature vector and a plurality of filters, and a second pooling layer for outputting a fourth feature vector by performing sub-sampling to the third feature vector.

10. The apparatus of claim 9 , wherein the first convolution layer and the second convolution layer perform the convolution operation using a hyperbolic tangent function or a rectified linear unit (ReLU) function as an activation function.

11. The apparatus of claim 9 , wherein the first pooling layer and the second pooling layer perform the sub-sampling using a max pooling function.

12. The apparatus of claim 9 , wherein the detection model further comprises one or more fully-connected layers connected to the second pooling layer; and an output layer for outputting a discrimination value of the debatability of the document from outputs of the one or more fully-connected layers.

13. The apparatus of claim 12 , wherein the output layer outputs the discrimination value using a softmax function as an activation function.

14. The apparatus of claim 8 , wherein the one or more computer readable media further include instructions that when executed cause the apparatus to convert each of the words included in the document into a one-hot vector and generate the embedding vector for each of the words by a product of the embedding matrix and the one-hot vector for each of the words.